Interpretable machine learning prediction of live birth after freeze-all FET cycles across transfer-order subgroups
BackgroundTransfer-order heterogeneity may affect live-birth prediction after freeze-all FET cycles, but existing prediction studies have rarely modeled first- and second-transfer records separately.MethodsWe developed and compared logistic regression (LR), support vector machine, random forest, XGB...
Сохранить в:
| Главные авторы: | , , , , , , , |
|---|---|
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Frontiers Media S.A.
2026-07-01
|
| Серии: | Frontiers in Endocrinology |
| Предметы: | |
| Online-ссылка: | https://www.frontiersin.org/articles/10.3389/fendo.2026.1868575/full |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|